1. Field of the Invention
The present invention relates to a robot behavior control system and method and a robot apparatus in which a robot is capable of acting autonomously to achieve realistic communication with users. In particular, the present invention relates to a behavior control system and method and a robot apparatus in which a robot is capable of selecting an appropriate behavior from a comprehensive consideration of conditions of the robot, such as the external environment detected by sensory recognition, such as visual or auditory recognition, and internal states including instincts and feelings.
More specifically, the present invention relates to a robot behavior control system and method and a robot apparatus in which a robot is capable of selecting an instinctive or reflexive situated behavior or any other behavior based on external environments and internal states. In particular, the present invention relates to a robot behavior control system and method and a robot apparatus in which a robot is capable of evolving by self-development through imitation, learning, etc.
2. Description of the Related Art
A mechanical apparatus which utilizes electric or magnetic actions to perform motions which resemble motions of human beings is referred to as a “robot.” It is said that the word robot is etymologically derived from the Slavic word “ROBOTA (slave machine).” In Japan, robots have become widespread since the end of 1960s, but most of them have been manipulators or industrial robots such as conveyor robots for the purpose of automated or unmanned production operations in factories.
Recently, research and development has advanced on the structure and stable walking control of legged mobile robots including pet robots which imitate body mechanisms and motions of quadrupled animals, such as dogs and cats, and “human like” or “humanoid” robots which imitate body mechanisms and motions of upright bipedal animals, such as human beings and apes. Thus, expectations on the practical use of such legged mobile robots have increased. Legged mobile robots are less stable and have more difficult posture control and walk control than crawler robots, but are advantageous in that they can realize flexible walking and running motions such as moving up and down the stairs and leaping over obstacles.
Other than industrial uses, uses of recent robot apparatuses include living uses, i.e., “symbiosis” uses with human beings or “entertainment” uses. Traditional toy machines have a fixed relationship between a user operation and a responsive motion, and it may be impossible to modify the motions of the toys according to the preference of users. As a result, users soon get tired of such toys that repeat the same motions. On the other hand, intelligent robot apparatuses have behavior models or learning models which originate from motions, and allow the models to be modified based on external input information, such as voices, images, or touch, to determine a motion, thereby realizing an autonomous thought and motion control. Such intelligent robot apparatuses further include an interface with human being using recognition technology, such as image recognition and speech recognition, and allow intelligent realistic communication.
In a typical behavior selection mechanism of robots or other real-time interactive systems, behaviors are sequentially selected in response to changes in the external environment detected by sensory recognition such as visual recognition and auditory recognition. In another behavior selection mechanism, the internal state of the system is managed using models of emotions including instincts and feelings, and behaviors are selected in response to changes in the internal state.
For example, the internal state includes an “instinct” aspect that corresponds to access to the limbic system in a living organism, an aspect derived from ethological models, such as innate desire and social desire, which corresponds to access to the cerebral neocortex, and a “feeling” aspect, such as joy, grief, anger, and surprise.
The internal state of the system changes not only when the external environment changes but also when a selected behavior is exhibited.
For example, Japanese Unexamined Patent Application Publication No. 2003-334785 discloses a robot apparatus that selects a behavior from a comprehensive consideration of the conditions of the robot, such as a result of recognition of an external environment through a visual sense or an auditory sense and an internal state including an instinct and a feeling.
The robot apparatus disclosed in this publication includes a plurality of behavior modules, each having a behavior evaluation section outputting an evaluation of a behavior of the robot apparatus responsive to an internal state or an external input, and a behavior instruction output section outputting an instruction for behavior execution of the robot apparatus. The robot apparatus determines an optimum behavior module from a comprehensive point of view in response to an external stimulus and a change in the internal state based on the evaluation obtained from the behavior evaluation section of each behavior module. Thus, concurrent evaluation is realized. Moreover, resource-based behavior selection is realized. That is, an evaluation of a behavior is output from a behavior module in a lower layer to a behavior module in a higher layer in the layered structure, and a behavior module is selected based on the evaluation and a resource used by the robot apparatus. Thus, concurrent selection is realized.
It is expectable that a more intelligent robot apparatus not only allows a robot to exhibit a behavior in the manner described above but also allows a robot to autonomously learn and to evolve by self-development.
Generally, learning mechanisms in robots and other automatic machines are implemented using neural networks or other mathematical models.
A neural network is a simplified simulation of the neural network connections of the human brain, and is a network of nerve cells or neurons that are connected through synapses via which signals are transmitted in one direction. Signals are communicated between neurons through synapses, and the synapse resistance, or weight, is appropriately adjusted to perform various information processing. Each neuron has synapse-weighted inputs from one or more other neurons, and outputs the sum of the inputs, which is modified using non-linear response functions, to another neuron. In neural-network-based control, non-linear problems, such as friction and viscosity, are overcome, and, due to its learning ability, there is no need for changing parameters.
For example, in a neural-network-based robot system, motion patterns are stored in association with symbols input by voices or the like (see, for example, Japanese Unexamined Patent Application Publication No. 2002-337075). In the robot system disclosed in this publication, a relatively long motion or any other motion is divided into segments, and the motion segments are stored in association with input symbols recognized by speech recognition or the like. It is possible to play back a motion in response to an instruction for a relatively long motion pattern. It is also possible to exhibit a motion similar to a desired motion by association with a symbol input by voice if the same motion as the desired motion has not been directly learned. An architecture that employs a recurrent neural network includes internal feedback connections, in which information transmitted in a loop previous to the current loop is held in the network, and deals with recording of time-series data.
In general, human beings and other animals after which robots are modeled learn through simulation, that is, imitation of parents (or caregivers), based on stimuli.